Categories
AI

Google’s Visual Inspection AI spots defects in manufactured goods

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out.


Google today announced the launch of Visual Inspection AI, a new Google Cloud Platform (GCP) solution designed to help manufacturers, consumer packaged goods companies, and other businesses reduce defects during the manufacturing and inspection process. Google says it’s the first dedicated GCP service for manufacturers, representing a doubling down on the vertical.

It’s estimated that defects cost manufacturers billions of dollars every year — in fact, quality-related costs can consume 15% to 20% of sales revenue. Twenty-three percent of all unplanned downtime in manufacturing is the result of human error compared with rates as low as 9 percent in other sectors, according to a Vanson Bourne study. The $327.6 million Mars Climate Orbiter spacecraft was destroyed because of a failure to properly convert between units of measurement, and one pharma company reported a misunderstanding that resulted in an alert ticket being overridden, which cost four days on the production line at £200,000 ($253,946) per day.

Powered by GCP’s computer vision technology, Visual Inspection AI aims to automate quality assurance workflows, enabling companies to identify and correct defects before products are shipped. By identifying defects early in the manufacturing process, Visual Inspection AI can improve production throughput, increase yields, reduce rework, and slash return and repair costs, Google boldly claims.

AI-powered inspection

As Dominik Wee, GCP’s managing director of manufacturing and industrial, explains, Visual Inspection AI specifically addresses two high-level use cases in manufacturing: cosmetic defection detection and assembly inspection. Once the service is fine-tuned on images of a business’ products, it can spot potential issues in real time, optionally operating on an on-premises server while leveraging the power of the cloud for additional processing.

Visual Inspection AI competes with Amazon’s Lookout for Vision, a cloud service that analyzes images using computer vision to spot product or process defects and anomalies in manufactured goods. Announced in preview at the company’s virtual re:Invent conference in December 2020 and launched in general availability in February, Amazon claims that Lookout for Vision’s computer vision algorithms can learn to detect manufacturing and production defects including cracks, dents, incorrect colors, and irregular shapes from as few as 30 baseline images.

But while Lookout for Vision counts GE Healthcare, Basler, and Sweden-based Dafgards among its users, Google says that Renault, Foxconn, and Kyocera have chosen Visual Inspection AI to augment their quality assurance testing. Wee says that with the Visual Inspection AI, Renault is automatically identifying defects in paint finish in real time.

Moreover, Google claims that Visual Inspection AI can build models with up to 300 times fewer human-labeled images than general-purpose machine learning platforms — as few as 10. Accuracy automatically increases over time as the service is exposed to new products.

“The benefit of a dedicated solution [like Visual Inspection AI] is that it basically gives you ease of deployment and the peace of mind of being able to run it on the shop floor. It doesn’t have to run the cloud,” Wee said. “At the same time, it gives you the power of Google’s AI and analytics. What we’re basically trying to do is get the capability of AI at scale into the hands of manufacturers.”

Trend toward automation

Manufacturing is undergoing a resurgence as business owners look to modernize their factories and speed up operations. According to ABI Research, more than 4 million commercial robots will be installed in over 50,000 warehouses around the world by 2025, up from under 4,000 warehouses as of 2018. Oxford Economics anticipates 12.5 million manufacturing jobs will be automated in China, while McKinsey projects machines will take upwards of 30% of these jobs in the U.S.

Indeed, 76% of respondents to a GCP and The Harris Poll survey said that they’ve turned to “disruptive technologies” like AI, data analytics, and the cloud to help navigate the pandemic. Manufacturers told surveyors that they’ve tapped AI to optimize their supply chains including in the management, risk management, and inventory management domains. Even among firms that currently don’t use AI in their day-to-day operations, about a third believe it would make employees more efficient and be helpful for employees overall, according to GCP.

“We’re seeing a lot of more demand, and I think it’s because we’re getting to a point where AI is becoming really widespread,” Wee said. “Our fundamental strategy is to make Google’s horizontal AI capabilities and integrate them into the capabilities of the existing technology providers.”

According to a 2020 PricewaterhouseCoopers survey, companies in manufacturing expect efficiency gains over the next five years attributable to digital transformations. McKinsey’s research with the World Economic Forum puts the value creation potential of manufacturers implementing “Industry 4.0” — the automation of traditional industrial practices — at $3.7 trillion in 2025.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Applied Materials brings AI and big data into semiconductor inspection machines

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.


Applied Materials has launched a new generation of optical semiconductor wafer inspection machines that incorporate big data and AI techniques.

These multimillion-dollar machines are used in chip factories that can cost $22 billion to build and generate even more revenue than that. Such factories send wafers through hundreds of manufacturing steps before they’re finished and sliced into individual chips that are used in everything electronic.

With a severe shortage of such chips during the pandemic, Applied Materials’ latest improvements to the machines are timely, as the AI techniques enable the new Enlight optical wafer inspection systems to automatically inspect more chips and detect more killer defects that can ruin chips. These kinds of inspection machines alone add up to a $2 billion market worldwide.

Applied Materials executives like CEO Gary Dickerson have been predicting for years that the recent advances in AI would prove transformative in semiconductor manufacturing, and that’s what’s playing out now, Keith Wells, group vice president at Applied Materials, told VentureBeat in an interview.

“We all know that AI and big data have the potential to transform every area of the economy,” Wells said. “Today, that’s now reality. We’re bringing AI and big data into the semiconductor manufacturing.”

The new inspection systems are the fastest-ramping tools in the history of Santa Clara, California-based Applied Materials, which is the largest maker of equipment used in semiconductor factories. The machines speed time to revenue and help a chipmaker earn more profits over the life of a manufacturing process.

“We believe this is the industry’s fastest high-end optical inspector that is 3 times faster, and it has the sensitivity to find these yield-critical defects,” Wells said. “We believe it has the ability to impact the economics.”

The challenges

Chip manufacturing is getting more expensive and complex.

Above: Chip manufacturing is getting more expensive and complex.

Image Credit: Applied Materials

The challenge is that the costs of inspecting increasingly miniaturized patterns on wafers are rising, and the inspections are also becoming more complex. A decade ago, chip factory costs were about $9 billion. Now they’ve doubled. Over the life of the factory, the chipmaker can depreciate the cost of the chipmaking equipment in the factories. But manufacturing delays and inspection failures can cause factories to go idle (and lose a ton of money) as engineers try to decipher the cause of failures.

When it comes to memory chips, a week’s downtime can knock down annual output by 2%. On top of that, the price of the chips drops rapidly over time, and so falling behind schedule can severely damage revenue, Wells said. Add to this the notion that the inspection machines are getting more complicated and more expensive to produce.

“You don’t make money until you start ramping in volume in the millions of chips,” Wells said.

Dan Hutcheson, CEO of market analyst firm VLSI Research, said in a statement that being able to quickly and accurately distinguish killer defects is something chip engineers have struggled with for more than three decades. He said Applied Materials’ Enlight system with ExtractAI technology is a breakthrough approach that solves this challenge and added that because the AI gets smarter the more the system is used, it helps chipmakers increase their revenue per wafer over time.

In an email, Hutcheson said that Enlight can cut yield loss (the percentage of a wafer lost to defective chips) by $2.6 million for every hour it trims off the time to respond to a deviation from normal yields. He said inspection accounts for about 10% of the cost in an advanced wafer fab, and the current cost of such fabs is about $22 billion. That’s almost as much as two aircraft carriers and 65 F22 Raptor jets.

Semiconductor technology is becoming increasingly complex and expensive. So reducing the time needed to develop and ramp advanced manufacturing process nodes can be worth billions of dollars to chipmakers around the world.

But not being able to inspect chips fast enough is a barrier to speed. That’s a problem because it is increasingly hard to focus lenses so that you can see the surface of a chip, where the circuits are as little as five nanometers — or five billionths of a meter — apart. The tiniest specs of dust can be like boulders on the surface of a wafer.

That’s where the inspection machines come in. They can use AI to detect anomalies on the surface of a chip and then automatically fix the errors, if possible, so that the nuisance particles don’t ruin the circuitry.

“We’re looking for the defects that are effectively going to kill the device,” Wells said.

Above: Chip complexity is making inspection harder.

Image Credit: Applied Materials

For instance, if two circuit lines get crossed, that will divert electrical signals and possibly short circuit an entire chip. The inspection system uses a state-of-the-art scanning electron microscope, which helps identify the signals coming off the optical inspector to do classification of the flaws, Wells said.

“We’re going to take that classified data, and we’re going to feed it into an AI algorithm, which we call ExtractAI,” Wells said.

The result is creating actionable data for customers that lets them solve problems faster than ever. In the past, chipmakers have deployed more primitive AI, where the classification engine is static. It doesn’t have the ability to learn and adapt automatically. But chipmaking processes, or recipes for building chips, change frequently.

“The next necessary step is to allow the AI to learn and adapt,” Wells said. “As the process changes, they can adapt.”

Applied Materials said that 3D transistor formation and multiprocessing techniques introduce subtle variations that can multiply to create yield-killing defects that range from vexing and time-consuming to root-cause.

The company is solving these challenges with a new playbook for process control designed to bring the benefits of big data and AI technology to the core of chipmaking technology. Applied Materials’ solution consists of three elements it claims work together in real time to find and classify defects faster, better, and more cost effectively than legacy approaches.

The Enlight Optical Wafer Inspection System

Above: Enlight, ExtractAI, and SemVision are part of Applied Materials’ new inspection process.

Image Credit: Applied Materials

The AI comes in to make a decision about whether to slow the production speed down and alert a human about a problem in a wafer that carries a varying degree of risk.

In development for five years, the Enlight system combines industry-leading speed with high resolution and advanced optics to collect more yield-critical data per scan. The Enlight system architecture improves the economics of optical inspection, resulting in a 3 times reduction in the cost of capturing critical defects compared to competing approaches. The system has a more robust optical system — including features that put the equivalent of sunglasses on the optical lenses — to focus quickly on the problem parts of a wafer surface.

By dramatically improving cost, the Enlight system allows chipmakers to insert many more inspection points in the process flow. The resulting availability of big data enhances “line monitoring,” statistical process control methods that can predict yield excursions before they occur, immediately detect excursions so that wafer processing can be halted to protect yields, and enable root-cause traceback to accelerate corrective actions and the return to high-volume manufacturing.

“There are a lot of imperfections that engineers might not care about that your optical inspector will find, but it may not be a killer defect,” Wells said. “So the challenge is to give the customer actionable data.”

ExtractAI technology

Developed by Applied Materials’ data scientists, ExtractAI technology solves the most difficult problem of wafer inspection: the ability to quickly and accurately distinguish yield-killing defects from the millions of nuisance signals or “noise” generated by high-end optical scanners. It has to take a million possible problems and reduce them to 1,000 that can be inspected more closely.

ExtractAI creates a real-time connection between the big data generated by the customer’s optical inspection system and the eBeam review system that classifies specific yield signals so that by inference, the Enlight system resolves all of the signals on the wafer map, differentiating yield killers from noise.

ExtractAI technology is incredibly efficient; it characterizes all of the potential defects on the wafer map after reviewing only 0.001% of the samples. The result is an actionable map of classified defects that accelerates semiconductor node development, ramp, and yield. The AI technology is adaptive and quickly identifies new defects during high-volume production while progressively improving its performance and effectiveness as more wafers are scanned.

The ExtractAI tech uses high-resolution scans to detect the worst problems. Once the actual defects are removed, the system learns to adapt to better detection techniques the next time around. ExtractAI can reduce the number of problem areas from about a million to just about 1,000 that will need a closer look or some action.

“We interrogate the data, and we’re actually learning and adapting our classifying defects in real time,” Wells said. “This is different from other approaches where the classifiers are static.”

SemVision eBeam Review System

Above: Enlight has a complex optical system.

Image Credit: Applied Materials

The SemVision system is the most advanced and widely used eBeam review technology in the world, as 1,500 systems are in place at chip factories throughout the world. The SemVision system trains the Enlight system with ExtractAI technology to classify yield-killing defects and distinguish defects from noise.

By working together in real time, the Enlight system, ExtractAI technology, and SemVision system help customers identify new defects as they are introduced into the manufacturing flow, enabling higher yields and profitability. The large installed base of SemVision G7 systems is already compatible with the new Enlight system and ExtractAI technology.

“We’ve seen over the last five years the rise in capital costs of these inspectors, making the economics difficult,” Wells said. “Customers have been reducing inspections in order to compensate for the increase in the cost of these tools. But unfortunately, when you reduce inspection points, you get yield problems. The industry wants a better economic value message around doing more inspection. And we’re trying to provide that.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
Game

Shiny Pokemon TCG Shining Fates unboxing and foil inspection

The Pokemon Trading Card Game released a new set here at the tail end of February called Shining Fates. Today we’re taking a look at a few different boxes and and unpacking a few packs to see what’s inside. This isn’t your average set – it’s relatively small, meaning you’ve got a better chance to “Catch Em All” than usual. This set contains not only standard rares, and foils, and Pokemon V cards, and VMAX Pokemon, but SHINY versions of each of these sorts of cards.

First, let’s take a moment to appreciate how wildly varied the illustrations continue to be here in this latest set of Pokemon TCG in February of 2021. We’ve got three versions of this Cramorant Pokemon here, each with wildly different abilities, illustrations, and executions.

There’s the basic Cramorant, that’s uncommon. Then there’s the rare SHINY Cramorant, with a whole new foil pattern (more on that below). Then there’s the Cramorant V, complete with two of the most epic moves we’ve ever seen on such a pathetic looking monster.

Next we’ve got Morpeko. This is a Shiny Rare Pokemon, one that features one of the cutest Pokemon in the whole Pokemon universe. You’ll see here how the Shiny Rare Pokemon features a new Shiny Pattern – physical, bumpy foil – that’s just lovely. It’s an explosion of shiny!

If you purchase one of the special edition tins available with this set, you’ll get a Shiny V Pokemon. In this tin, we’ve got Eldegoss V, with a similar pattern to that of the Rare Shiny Pokemon.

This Shiny V Pokemon also has an explosion of foil, but the center of said explosion is lower, since the illustration on the card takes up the entirety of the card, rather than just the top half. That’s a very nice touch!

Another sort of Rare Pokemon you can attain in this set is the “AMAZING RARE”. This sort of Rare is given the multicolored star with the “A” in the middle, right down there at the bottom of the card.

This Yveltal features a whirlwind of color that reaches beyond the standard Pokemon Rare illustration frame, making this rare feel extra particularly special. This Pokemon’s ability is… pretty good, too: “Your opponent’s Active Pokemon is Knocked Out.” Easy!

You could also get a SHINY VMAX! This monster is so shiny, so foil, so VMAX, so very reflective of all the colors. This one might be a little more effort to play than it’s worth, but it still looks lovely.

The Pokemon TCG Shining Fates Trainers Box has a massive VMAX Eevee and a color scheme that includes black and dark peanut butter – orange/brown. This pack’s special Eevee card protectors feel slightly more matte than previous Trainers Box protectors.

The Shining Fates set is out in stores right this minute, with prices pretty much equivalent to those of the most recent sets over the past couple years. And they look amazing, packs to cards to accessories and back again.

Repost: Original Source and Author Link